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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Heng Wang Schmid, C. |
| Copyright Year | 2013 |
| Description | Author affiliation: LEAR, INRIA, Grenoble, France (Heng Wang; Schmid, C.) |
| Abstract | Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art. |
| Sponsorship | IEEE Comput. Soc. |
| Starting Page | 3551 |
| Ending Page | 3558 |
| File Size | 2401490 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479928408 |
| ISSN | 15505499 |
| DOI | 10.1109/ICCV.2013.441 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-01 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Trajectory Cameras Optical imaging Adaptive optics Vectors Detectors Feature extraction |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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